FSTL1 is a prognostic marker and promotes invasion and metastasis of colon cancer

Abstract

Background

Colorectal cancer (CRC) stands as the most common gastrointestinal malignancy, with the Follistatin-like 1 (FSTL1) gene associated with unfavorable outcomes in diverse cancer forms. Nonetheless, the precise mechanisms through which FSTL1 influences CRC pathogenesis and its associated multi-omics characteristics remain unclear.

Methods

This study leveraged RNA sequencing and single-cell RNA sequencing (scRNAseq) data from the Gene Expression Omnibus (GEO) database to scrutinize the expression profile, prognostic significance, and clinicopathological relevance of FSTL1 in colorectal cancer. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis were conducted to delve into the potential biological functions of FSTL1 in colorectal cancer and its associated pathways. The ESTIM algorithm and Tumor Immune Estimation Resource (TIDE) database were utilized to investigate FSTL1 expression in immune cell infiltration and immune checkpoints. Mutation characteristics of FSTL1 were elucidated using the cBioPortal database. The pRRophetic package was employed to identify potential chemotherapeutic drugs targeting FSTL1. Expression levels of FSTL1 in colon cancer and adjacent normal tissues were evaluated using the Human Protein Atlas (HPA) database. Furthermore, single-cell sequencing technology and cell communication were employed to assess the immunogenomic features of FSTL1 in the tumor microenvironment. In vitro experiments were conducted to validate the efficacy of FSTL1.

Results

FSTL1 levels were markedly higher in colorectal cancer tissues compared to healthy tissues, correlating significantly with unfavorable patient outcomes. Elevated FSTL1 expression was closely linked to key biological processes including the PI3K pathway, cell adhesion, proliferation, migration, and extracellular matrix remodeling. Furthermore, FSTL1 expression exhibited strong associations with immune cell levels and the tumor immune microenvironment in colorectal cancer. Notably, FSTL1 mutations predominantly comprised missense mutations and displayed significant correlations with various immune checkpoints and low methylation levels. Axitinib emerged as a promising targeted therapeutic option for patients with high FSTL1 expression. Analysis from the HPA database confirmed elevated FSTL1 expression in colorectal tissues compared to adjacent normal tissues. Single-cell sequencing analysis identified stromal cells as the primary source of abundant FSTL1 expression, potentially influencing tumor microenvironment remodeling by myeloid and B cells via the APP-CD74 pathway. Downregulating FSTL1 expression in colon cancer cells suppresses the proliferation, motility, and invasiveness of SW480 and HCT116 colon cancer cells.

Conclusion

In summary, the findings of the study indicate that elevated FSTL1 expression could serve as a prognostic biomarker for unfavorable outcomes in colorectal cancer (CRC) diagnosis and may also identify potential targets for immunotherapy in CRC.

Introduction

Colorectal cancer (CRC) is one of the most common tumors and the third leading cause of cancer-related deaths globally [1, 2]. Surgical resection is the mainstay treatment for colorectal cancer, while neoadjuvant chemoradiotherapy is the standard approach for locally advanced rectal cancer. This regimen is frequently complemented by targeted therapy and immunotherapy directed at distinct molecular subtypes such as MS-H, yet the overall survival rate for patients with colorectal cancer has not significantly improved in recent years [3, 4]. Distant metastasis [5] is the main reason people with colorectal tumors die. The accumulation of various genetic and epigenetic changes causes this. In recent years, immunotherapy has quickly become an effective treatment for people· with colorectal cancer [6].

The molecular mechanisms underlying immune dysfunction remain unclear. Emerging research has elucidated a strong correlation between the tumor microenvironment (TME) and immune dysfunction. Tumor-infiltrating lymphocytes (TILs), such as tumor-associated macrophages (TAMs) and tumor-infiltrating neutrophils (TINs), have unique histological features that can tell the difference Variability among malignant tumor types and patients is common. Studies have demonstrated the significant prognostic impact of tumor-infiltrating lymphocytes (TIL).treatment of malignant tumors. It is very important to understand the immunophenotype of tumor-immune interactions because this will help find new Immunotherapy is directed towards various malignant tumors, with Follistatin-like 1 (FSTL1) being a 308-amino acid secreted glycoprotein belonging to the SPARC family. follistatin-like and calcium-binding domains that are outside of cells. In 1988, researchers cloned FSTL1 from the synovial tissue of rheumatoid arthritis patients, and detected its abundant presence in the serum and synovial fluid of these patients Moreover, FSTL1 is notably expressed in fibrotic macrophages of both human and mouse livers. It interacts directly with pyruvate kinase M2 (PKM2) via its FK domain, facilitating M1 polarization, inflammation, and augmenting glycolysis [7]. Inflammation has been shown to play a crucial role in the progression of various tumor types [8]. Previous studies have revealed that FSTL1 has contrasting impacts on the promotion and suppression of different types of tumors. Ni X et al [9]. reported that in non-small cell lung cancer (NSCLC), the downregulation of FSTL1 expression leads to enhanced tumor cell proliferation, migration, and invasion. These results indicate that FSTL1 acts as a tumor suppressor in the advancement of non-small cell lung cancer (NSCLC). Conversely, Lau showed that esophageal squamous cell carcinoma (ESCC) exhibits abnormal amplification of FSTL1 copy number, leading to a positive impact. The correlation between its expression level and the promotion of esophageal squamous cell carcinoma (ESCC) occurrence and metastasis is mediated through the NF-κB and BMP pathways [10]. This evidence indicates that Follistatin-like 1 (FSTL1) is a pivotal oncogenic factor in esophageal squamous cell carcinoma (ESCC), exerting a substantial influence on both tumor immunity and the advancement of tumor cells. However, the prognostic implications and involvement of FSTL1 in immune infiltration within colorectal cancer are currently not well-defined. Further research is necessary to clarify the potential involvement and underlying mechanisms of FSTL1 in tumor advancement and immune responses.

This study used bioinformatics techniques to find out how much FSTL1 was expressed in CRC. It also used the GEO database and the CIBERSORT method to look into the connection between FSTL1 and immune cell infiltration and immune checkpoints. We conducted protein-protein interaction network (PPI), differentially expressed gene (DEG), and functional enrichment analyses to ascertain the role of The role of FSTL1 in colorectal cancer (CRC) was investigated using single-cell RNA sequencing (scRNA-seq) to elucidate its impact on the immune milieu within the tumor microenvironment. Subsequent in vitro experiments were performed to validate its functional significance. This research identifies FSTL1 as a potential prognostic biomarker in CRC associated with immune responses, shedding light on its interactions within the tumor microenvironment and its implications in colorectal cancer pathogenesis.

Methods

Data acquisition

All data were sourced from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).Forbulk RNA sequencing data, normalization methods such as the normalizeBetweenArrays function in the limma R package were employed to mitigate technical variations across samples. Differential gene expression analysis between tumors and samples was conducted using tools like the limma package for microarray data or DESeq2 for RNA-Seq data, with significance thresholds set at |log fold change| >1 and adjusted p-value (Benjamini-Hochberg) < .05. The datasets utilized were as follows: Dataset 1 (GSE39582) comprised 56 colon cancer samples and 19 control tissue samples (colonic mucosa) for gene expression analysis. Dataset 2 (GSE1615) included 32 colon cancer patients along with their corresponding survival data for survival analysis. Dataset 3 (GSE29621) consisted of 65 colon cancer patients and their matched survival information for survival analysis. Additionally, Dataset GSE17841 contained single-cell sequencing data from 62 colon cancer patients; refer to section 2.15for further details.

Survival and clinical analysis

In the case of datasets comprising survival information, the patients with available survival data were extracted and subjected to a survival analysis, which was conducted with the aid of the “survival” and “survminerpackages”. Subsequently, Kaplan-Meier survival curves were generated. The median expression level of FSTL1 was employed as a criterion for differentiating between high and low expression groups. To assess the clinical significance of the FSTL1 gene, clinical data were employed, and violin and box plots were created using the “ggstatsplot” package.

Differential analysis and enrichment analysis

A differential analysis was performed on the high and low expression groups of

FSTL1, utilizing the limma package to compare gene expression levels between these two groups. The screening criteria were established with an absolute logFC value exceeding 1 and a corrected P-value below 0.05. Differentially expressed genes underwent Gene Ontology (GO) [11]enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) [12]enrichment analysis, both conducted using the clusterProfiler package. Visualization was accomplished through the ggplot2, ComplexHeatmap, and circlize packages. The STING database (https://cn.string-db.org/) was employed to analyze protein-protein interaction relationships among differentially expressed genes, while the MCODE plugin of Cytoscape software (version 3.9.1) was utilized to identify the top three core protein interaction networks within these differentially expressed genes.

Immune-related analysis

The abundance analysis of immune cells and immune processes is conducted based on the single-sample Gene Set Enrichment Analysis (ssGSEA) method, with specific operations performed using the Gene Set Variability Analysis (GSVA) and GSEABase packages. Correlations are deemed statistically significant when the absolute value of the correlation coefficient exceeds 0.3 and the p-value is less than 0.05. The Estimate package is employed for the analysis of immune microenvironments, while the ggb-eeswarm package is utilized for the generation of related visualizations. The gene mutation data was obtained from the cBioportal database (https://www.cbioportal.org). The data pertaining to immunotherapy were obtained from the TIDE database (http://tide.dfci.harvard.edu/). The risk heatmaps were generated using the pheatmap package. The ggstatsplot package was employed to examine the correlation in expression between FSTL1 and classical immune checkpoint molecules. Furthermore, data pertaining to the methylation ofFSTL1 were obtained from the UALCAN database (https://ualcan.path.uab.edu/).

Drug sensitivity analysis

The median expression level of FSTL1 was used to classify samples into high and low expression groups. A screening for chemotherapy and targeted drugs associated with FSTL1 was conducted by evaluating the IC50 values between the two groups. The data were analyzed using the packages pRRophetic, ggplot2, and ggpubr.

Single-cell sequencing analysis

Single-cell data analysis was conducted based on the Seurat 4.2.2 version. In Particular, we did quality control on the dataset after it was loaded to get rid of cells whose nFeature_RNA was less than 500, greater than 5000, or had a mitochondrial proportion higher than 10%. The SCTransform function was employed to standardizeand normalize the data. The top 15 principal components were selected for dimensionality reduction and clustering, and the RunUMAP function was utilized to compute the dimensionality reduction clustering results. Single R was used for cell annotation. The FindAllMarkers function was applied to analyze marker genes for various cell types. The DoubletFinder function was utilized for doublet cell removal. The “CellChat” R package (version 1.5.0) was utilized to elucidate potential mechanisms of cell-cell communication within the single-cell scope. The “createCellChat” function was employed to construct a CellChat object, and the “aggregateNet” function was utilized to describe the signals emitted from each cell type. The netVisual_circle function was employed for the visualization of the number of cell-cell interactions and their respective weights.

Cell culture and transfection

The colon cancer cell lines HCT116 and SW480 were procured from Fuheng Cell Biology Co., Ltd. The cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum (FBS). All cell lines were incubated at 37 °C under 5% CO2 conditions. Qingke Biotechnology Co., Ltd. designed the FSTL1 sequence and synthesized the siRNA. Following the directions, Lipofectamine 3000 was used for transfection, and the cells were kept at 37°C with 5% CO2 for 48 hours. The study was divided into three groups: transfection with si-FSTL1# 1, si-FSTL1#2, and si-NC.

Real-time quantitative PCR detection (qRT-PCR)

Total RNA was extracted from each group of cells, and samples exhibiting a D260 nm/D280 nm ratio of 1.8 to 2.0 were selected for subsequent PCR amplification. A total of 800 ng ofRNA was required to synthesize cDNA, with 20 μL of enzyme-free water added to achieve the optimal volume. The instructions provided with the reverse transcription kit were followed, and a 200 μL enzyme-free EP tube was placed into the instrument. The temperature settings were set to 37°C for 15 minutes and 85°C for 5 seconds. This resulted in the conversion of the sample into cDNA. The GAPDH gene was selected as the internal reference, and real-time quantitative PCR was performed using the SYBR Premix Ex Taq (TAKARA) kit. The reaction system consisted of 6.25 μL of SYBR Premix Ex Taq, and the PCR amplification conditions were set as follows: 95 °C for 30 seconds, followed by 40 cycles of 95 °C for 5 seconds and 60 °C for 30 seconds. The relative expression level of the target gene was epresented by the 2-ΔΔCt value. The primer sequences are as follows:

FSTL1-F:5ʹGTATCCAGACCAGGAGAACAACAAG-3ʹ;

FSTL1-R:5ʹCTTGAGAAACTCTTGGAAGCTGAGT-3ʹ;

Detection of cell proliferation using the CCK-8 assay

The cells from the si-NC and si-FSTL1# 1, si-FSTL1#2 groups were collected, digested, and counted. Subsequently, the cells were seeded into 96-well plates at a density of 3×10³cells per well, with four replicate wells for each group. The proliferative capacity of HCT116 and SW480 cells was evaluated at three distinct time points. At 24, 48, and 72 hours. For each well, 10 μL of CCK-8 reagent was added, and the plates were incubated in a 37°C incubator under dark conditions for two hours. Subsequently, the plates were gently shaken to ensure uniformity, and then placed on an enzyme-linked immunoassay reader at a wavelength of 450 nm to measure the optical density (OD) values of each well. Subsequently, growth curves were plotted.

Cell scratch healing experiment

Following transfection, cells from each group were collected, digested, and counted. Subsequently, the cells were seeded into 6-well plates at a density of 4×10s cells perwell. Once the cells had reached 80-90% confluence, a 10 μl pipette tip was placed over each well, and a straight line was drawn vertically and uniformly along a ruler. The cells situated beneath the line were then washed with PBS, after which 5% fetal bovine serum-containing culture medium was added. At 0 and 24 hours, the cells were imaged using an inverted optical microscope to document the extent of migration.

Cell invasion assay

Following transfection, the cells from each group were collected and digested for counting. Matrigel was diluted with serum-free culture medium at a volume ratio of 1:8. The diluted Matrigel was then put on the upper chamber membrane of the Transwell chamber in a 50 μL amount. After the gel solidified, 200 μL of cell suspension containing 1×10⁵cells per well was seeded into the upper chamber. The lower chamber was filled with 600 μL of cell culture medium containing 20% fetal bovine serum. The chamber was then incubated in a 37°C incubator for 24 to 48 hours. After the incubation period, we fixed the cells with 4% paraformaldehyde solution for 20 min and then stained them with 0.1% crystal violet solution for an additional 20 min. Finally, three fields were selected under an inverted optical microscope for photography and recording, and the number of cells that had migrated through the chamber membrane was counted.

Data analysis

All statistical analyses in the study were conducted using R language (version 4.1.1). Pearson correlation was employed to calculate the correlation. Kaplan-Meier survival analysis was utilized to estimate survival outcomes, while multiple comparisons among groups were conducted using one-way ANOVA. Pairwise comparisons within groups were performed using the LSD-t test. Statistical significance was defined as P < 0.05, *P < 0.01, and ***P < 0.001.

Result

A flowchart containing the detailed procedures of this study is presented in Fig. 1

Fig. 1.

Flowchart

FSTL1 is a significant carcinogenic factor in colon cancer

The dataset GSE39582 comprises control and colon cancer tissues. Analysis reveals significantly higher abundance of FSTL1 in colon cancer cells compared to adjacent healthy cells (P <0.01), as depicted in the half-violin box plot (Fig. 2A). High FSTL1 expression correlates with decreased survival rates in the GEO microarray datasets GSE16125 and GSE29621 (P <0.01), as illustrated in Kaplan-Meier survival analysis, indicating FSTL1 as a potential prognostic marker for colon cancer (Fig. 2B, C). Elevated FSTL1 levels are associated with advanced clinical staging parameters, including tumor staging, AJCC staging, and TNM staging, implying a higher likelihood of adverse clinical outcomes in patients with high FSTL1 expression (Fig. 2D–I). These findings collectively suggest a significant role for FSTL1 as a potential oncogenic factor in colon cancer.

Fig. 2.

Expression of FSTL1 in colon cancer and its clinical significance. ABoxplot illustrating the expression differences ofFSTL1 in normal and colon cancer tissues using data from the GEO database (GSE39582). B, CSurvival differences associated with different expression levels of FSTL1 in two GEO datasets (GSE16125 and GSE29621). DKRAS mutation differences at different expression levels ofFSTL1. EClinical stage differences at different expression levels ofFSTL1. F AJCC stage differences at different expression levels of FSTL1. GT stage differences at different expression levels ofFSTL1. (H) N stage differences at different expression levels of FSTL1. I M stage differences at different expression levels ofFSTL1.* p<0.05; ** p<0.01; *** p<0.001.;NS: not statistically significant.

Potential biological functions and pathways of FSTL1 in CRC

Given the adverse clinical prognosis associated with FSTL1, we conducted a differential expression analysis focusing on individuals exhibiting high FSTL1 expression to elucidate its impact on biological processes. The analysis, depicted in a volcano plot, revealed 268 significantly upregulated genes in the high FSTL1 expression group (Fig. 3A), primarily implicated in cell adhesion, extracellular matrix composition, and cell proliferation (Fig. 3B). Subsequent KEGG enrichment analysis unveiled the involvement of these upregulated genes in pathways such as PI3K and the complement cascade (Fig. 3C), indicating FSTL1's pivotal role in tumor growth and immune system modulation. These differentially upregulated genes demonstrated notable protein-protein interaction relationships, with MCODE identifying three core protein interaction networks. Particularly noteworthy was the first network, where the average protein connectivity exceeded 0.4 (Fig. 3D–F).

Fig. 3.

Functional annotation ofFSTL1 in patients with colon cancer. A Volcano plot illustrating differentially expressed genes (DEGs) between colon cancer patients with high and low FSTL1 expression. B Radar chart visually presenting the representative gene ontology (GO) functional enrichment among upregulated DEGs in colon cancer with high FSTL1 expression. C Bubble chart visually displaying the representative Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways among upregulated DEGs in colon cancer with high FSTL1 expression. D–F PPI interaction network of differentially expressed genes upregulated by high FSTL1 expression. * p<0.05; ** p<0.01; *** p<0.001; NS not statistically significant.

Correlation between FSTL1 expression and immune infiltration levels in CRC

The reshaping of the tumor microenvironment (TME) is a crucial process in the development and progression of tumors. A study using ssGSEA immunoanalysis found a strong positive relationship between FSTL1 and many immune cells, including neutrophils, Treg cells, and TIL cells. This suggests that FSTL1 not only causes a strong inflammatory response but also stops T cells from fighting tumors (Fig. 4A–L). The immune microenvironment in colon cancer was shown by estimate analysis. Higher levels of FSTL1 expression were linked to higher stromal scores, immune scores, and microenvironment scores (Fig. 4M–O). This indicates that FSTL1 is a gene that highly activates the immune microenvironment, potentially promoting tumor progression through excessive activation of immune-inflammatory infiltration.

Fig. 4.

The correlation between FSTL1 expression and immune cell infiltration in colon cancer. A–L The scatter plot shows that FSTL1 expression is significantly positively correlated with macrophages, neutrophils, Treg cells, CCR cells, mast cells, T helper cells, etc. Comparison of StromalScore, ImmuneScore, and EstimateScore scores between the high-expression and low-expression groups of (M-O) FSTL1;*p<0.05; ** p<0.01; *** p<0.001; NS not statistically significant

The mutational status of the FSTL1 gene in colorectal cancer (CRC) and its association with immune checkpoint (ICP) genes

The cBioPortal database reveals the mutation profile of the FSTL1 gene across four colon cancer cohorts. FSTL1 predominantly exhibits missense mutations, with mutation rates ranging from 5 to 3% (Fig. 5A). The TIDE database analysis of 13 immune-related groups shows that FSTL1 expression is significantly negatively correlated with cytotoxic T lymphocyte (CTL) infiltration (P <0.05), which in turn positively correlates with tumor risk (Fig. 5B). The expression of immune checkpoint molecules largely guides the use of immune checkpoint inhibitors. Notably, FSTL1 exhibits a strong negative relationship with both PD-1 and CTLA-4(P <0.05), suggesting that patients with high FSTL1 expression may not be optimal candidates for immune checkpoint inhibitor treatment (Fig. 5C–E). Furthermore, tumor tissues from both colon and rectal cancer exhibit lower methylation levels compared to control groups, potentially due to the high expression of FSTL1 in these tumors (Fig. 5F–G).

Fig. 5.

The mutation status ofFSTL1 in colorectal cancer and its correlation with immune checkpoints. A The cBioPortal tool was utilized to display the mutation frequency of FSTL1 across four colorectal cancer cohorts. B A heatmap illustrates the correlation between FSTL1 and the CTL immune cohort. C–E Demonstrates the correlation between FSTL1 and three immune checkpoint molecules. F–G Shows the methylation levels ofFSTL1 in colorectal cancer.

FSTL1 predicts potential chemotherapeutic target drugs

For patients with FSTL1 who may not be suitable for immunotherapy checkpoint inhibitor treatment, we employed the pRRophetic algorithm to identify potential chemotherapy-targeted drugs that could be efficacious in FSTL1-expressing tumors. Individuals with elevated FSTL1 expression demonstrate a greater propensity to respond to six specific pharmaceutical agents, including axitinib and cisplatin. In contrast, patients with low FSTL1 expression may respond more favorably to three drugs, including sorafenib (Fig. 6A–I).

Fig. 6.

Drug sensitivity analysis. A–I Box plots of IC50 values for potential targeted chemotherapeutic drugs screened from the low FSTL1 expression group and the high and low FSTL1 expression groups.

The analysis of the FSTL1 immune landscape is based on single-cell sequencing

To investigate the cell types expressing FSTL1, single-cell sequencing data from colon cancer (GSE178341) was analyzed. Following quality control and normalization, 370,114 colon cancer cells were identified (Fig. 7A). Utilizing UMAP plots for dimensionality reduction clustering, seven distinct cell types were delineated: epithelial cells (EPCAM, KRT18, KRT19), myeloid cells (S100A8, S100A9, C1QB, C1QA, LYZ, FCER1G), B cells (CD79A, CD79B, MS4A1), plasma cells (JCHAIN, MZB1, IGKC), T/NK/ILC cells (CD3D, CD3E, NKG7, GLNY, IL7R), mast cells (CAP3, TPSB2, TPSAB1), and stromal cells (IGFBP7, COL1A1, COL1A2, DCN, VWF, ACTA2)(Fig. 7B). FSTL1 expression was notably prominent in stromal cells, indicating a potential role in promoting tumor cell proliferation and migration through stromal cell modulation (Fig. 7C, D). Stromal cells exhibited heightened intercellular signaling compared to other cell types (Fig. 7E), suggesting their involvement in shaping the tumor microenvironment through signal transduction. Specifically, stromal cells demonstrated significant interaction with myeloid and B cells via the APP-CD74 ligand receptor pair, known for antigen presentation, implying a potential regulatory role of FSTL1 in colon cancer development by modulating stromal cells to impact antigen presentation processes (Fig. 7F).

Fig. 7.

Single-cell sequencing analysis and cell communication analysis of FSTL1 in colon cancer. A Annotate the colon cancer single-cell dataset GEO (GSE178341) as seven types of UMAP plots. B Display a bubble plot of marker genes corresponding to different cell types. C, D Density plots and feature plots respectively show the expression levels ofFSTL1 in different cell types. E Display the cell communication intensity of FSTL1 in different cells. F A bubble plot shows the regulation of receptors and ligands between different cells by FSTL1.

FSTL1 secondary clustering analysis is based on single-cell sequencing

Given the predominant expression of FSTL1 in stromal cells, we performed a focused secondary clustering analysis exclusively on stromal cells to delve deeper into the influence of FSTL1 on these cells. Initially, by fine-tuning resolution parameters, we identified a total of 16 distinct stromal cell clusters, numbered from to 15 (Fig. 8A). These clusters were annotated using established markers for endothelial cells (VWF, CD34, CLDN5), pericytes (RGS5, HIGD1B, MCAM), fibroblasts (COL1A1, DCN, LUM), and smooth muscle cells (ACTA2) (Fig. 8B, C). Notably, FSTL1 did not exhibit heightened expression in any specific cell type (Fig. 8D). Consequently, stromal cells were categorized into FSTL1-positive and FSTL1-negative groups using a normalized cutoff value. Interestingly, besides smooth muscle cells, the proportion of FSTL1-positive cells in all other cell types exceeded 50%, with fibroblasts constituting nearly 80% of the total (Fig. 8E, F). This observation suggests a significant role for FSTL1 in promoting the involvement of fibroblasts in tumor microenvironment remodeling. Expression patterns revealed distinct profiles for different cell types, with no distinct clustering of FSTL1-positive cells. However, some similarity was noted between FSTL1-positive and FSTL1-negative cells in pericytes and smooth muscle cells, particularly regarding the expression of the top 10 genes. This implies that FSTL1-positive cells may possess the ability to communicate with each other and support tumor progression (Fig. 8G, H).

Fig. 8.

Expression and characteristics of FSTL1-positive and negative cells in different types of stromal cells. A UMAP clustering analysis is presented, dividing stromal cells into 16 clusters. B The expression of specific marker genes in four stromal cell subtypes is illustrated through a bubble plot. C UMAP clustering analysis is demonstrated, annotating the secondary clustering of stromal cells as endothelial cells (VWF, CD34, CLDN5), pericytes (RGS5, HIGD1B, MCAM), fibroblasts (COL1A1, DCN, LUM), and smooth muscle cells. D The expression levels ofFSTL1 in different cell types are further illustrated through a scatter plot. E, F The proportions ofFSTL1-positive and negative cells in different types of stromal cells are displayed through a scatter plot and line chart. G A heatmap illustrates the aggregation of FSTL1-positive and negative cells in different types of stromal cells. H A heatmap displays the expression of marker genes in FSTL1-positive and negative cells across different types of stromal cells

The impact of the FSTL1 gene on the differentiation trend of stromal cells and the enrichment analysis conducted at the single-cell level

To further investigate how FSTL1 affects the differentiation trends of stromal cells, the VECTOR algorithm was employed to calculate the RNA splicing rates of different stromal cells. The results revealed that, regardless of the type of stromal cell, there is a trend toward differentiation from FSTL1-positive cells to FSTL1-negative cells. Some stromal cells are still in the early stages of differentiating, which suggests that high FSTL1 levels help tumors grow in the early stages (Fig. 9A). GSEA analysis conducted at the single-cell level on FSTL1-positive and negative cells indicated that FSTL1-positive cells upregulated biological processes and signaling pathways related to angiogenesis, cell adhesion, and extracellular matrix receptors, while downregulating antitumor responses such as antigen presentation and immune cell activation (Fig. 9B, C). These findings confirm that FSTL1-positive cells are key factors promoting the progression of colorectal cancer. We also used the scMetabolism algorithm to look at metabolic differences between cells that were FSTL1-positive and cells that were FSTL1-negative. Interestingly, the two groups exhibited distinct metabolic characteristics. FSTL1-positive cells primarily activated the degradation of glycosaminoglycans and other glycans (Fig. 9D). Upon searching the Human Protein Atlas (HPA) database (https://www.proteinatlas.org), we found that the protein level of FSTL1 in colon cancer tissues was significantly elevated compared to adjacent non-cancerous tissues (Fig. 9E).

Fig. 9.

Differentiation trends under different expression patterns of FSTL1, as well as GSEA and metabolic pattern analysis. A Shows the trajectory diagram ofFSTL1 differentiation rates in different stromal cells. B, C GSEA analysis of FSTL1-positive and negative cells based on single-cell level. D Bubble plot evaluating the metabolic patterns of FSTL1-positive and negative cells using scMetabolism score. E The expression of FSTL1 in colon cancer and adjacent tissues was examined using the HPA online database.

Upregulated FSTL1 in colon cancer promotes tumor cell proliferation, invasion and migration

Based on the above findings, we conclude that FSTL1 is a key gene that promotes cancer development and exhibits significant immune-related characteristics. qRT-PCR assays showed that FSTL1 expression was increased in human colon cancer cells SW480 and HCT116 compared to normal colon epithelial cells NCM460 (Fig. 10A). In addition, we transfected siRNA#1 and 2# into SW480 and HCT116 cells and evaluated the transfection efficiency using qRT-PCR assays (Fig. 10B, C). The cell counting kit-8 (CCK-8) assay showed that silencing the FSTL1 gene inhibited the proliferation of colon cancer cells SW480 and HCT116 (Fig. 10D, E). Cell scratch assay and Transwell assay further showed that silencing the FSTL1 gene significantly inhibited the migration and invasion ability of colon cancer cells SW480 and HCT116 (Fig. 9F–I)These data suggest that FSTL1 plays a positive regulatory role in colon cancer cell biology.

Fig. 10.

Interference with the expression of FSTL1 can inhibit the proliferation, invasion, and migration of colon cancer cells. A qRT-PCR was used to detect the expression level of FSTL1 in normal colon epithelial cells and colon cancer cell lines. B, C The knockout efficiency of FSTL1 in colon cancer cells SW480 and HCT116 was detected by qRT-PCR. D, E CCK8 assay showed that knockout of FSTL1 gene inhibited the proliferation of colon cancer cells SW480 and HCT116. Knockout of the F, I FSTL1 gene inhibits the migration and invasion ability of colon cancer cells SW480 and HCT116. *p<0.05;**p<0.01;***p<0.001;NS: Not statistically significant

Discussion

Colorectal cancer (CRC) is a prevalent gastrointestinal malignancy, responsible for 9% of cancer-related deaths in men and 8% in women. Despite advancements in surgical and chemotherapeutic interventions that have improved clinical outcomes for CRC patients, overall prognosis remains suboptimal [13]. Therefore, there is a critical need to identify superior early diagnostic biomarkers, novel therapeutic targets, and to establish an effective system for prevention, early detection, and treatment [14]. The prognosis of CRC patients is influenced by various factors, with the tumor microenvironment (TME) playing a pivotal role. The TME is a multifaceted milieu comprising tumor cells, immune cells, inflammatory cells, tumor-associated fibroblasts, stromal tissue, and diverse chemokines [15–17]. Immune cells within the TME often support tumor progression by releasing signaling molecules that promote angiogenesis and immune evasion [18–20]. Studies have demonstrated that distinct cell types within the TME directly contribute to tumor initiation and growth, consequently impacting patient prognosis [21–23]. Recent investigations have implicated FSTL1 as a critical factor in the advancement of certain tumors, with mutations in this gene enhancing tumor invasiveness and progression. Nevertheless, the expression profile of FSTL1 in CRC and its role in modulating the tumor immune microenvironment remain incompletely characterized.

This study aimed to investigate the involvement of FSTL1 in the development and advancement of colorectal cancer (CRC). Analysis of RNA-Seq data from the GEO dataset GSE39582 revealed a significant upregulation of FSTL1 expression in CRC tumor tissues compared to adjacent normal tissues. Prognostic assessment of FSTL1 using two independent GEO datasets (GSE16125 and GSE29621) demonstrated a significant association between elevated FSTL1 levels and reduced overall survival (OS) rates in CRC patients. Furthermore, FSTL1 expression correlated closely with advanced clinical stages of CRC based on the American Joint Committee on Cancer (AJCC) and Tumor-Node-Metastasis (TNM) staging systems, suggesting its potential contribution to CRC progression, including tumor proliferation and metastasis. To delve deeper into the functional role of FSTL1, we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction (PPI) analyses on differentially expressed genes in the high FSTL1 expression group from the GSE39582 dataset. GO analysis revealed the involvement of FSTL1 and its associated genes in biological processes such as cell adhesion, proliferation, and extracellular matrix organization. KEGG analysis highlighted significant enrichment of the PI3K signaling pathway and complement cascade in the high-FSTL1 group. The PI3K/AKT/mTOR pathway, known for its frequent activation in human cancers, is strongly linked to tumorigenesis and therapeutic resistance [24]. Additionally, PPI analysis indicated robust interaction among co-expressed genes upregulated by FSTL1, with a connectivity score exceeding .4, supporting the notion of FSTL1's functional role in the tumorigenesis-promoting signaling network. Functional assessment of FSTL1 in CRC cell biology involved siRNA-mediated knockdown in HCT116 and SW480 cell lines. Subsequent CCK8, Transwell, and wound-healing assays demonstrated that FSTL1 silencing significantly reduced cell proliferation and invasion. These findings confirm the oncogenic regulatory role of FSTL1 in CRC and suggest its potential as a therapeutic target. A hallmark of cancer is the progressive accumulation of somatic mutations driving tumorigenesis, resulting in a distinct mutational profile that varies across cancer types, individual patients, and even different tumor lesions within the same patient. Numerous genetic alterations have been linked to the onset and progression of CRC [25, 26]. Comprehensive molecular analysis, as recommended by the National Comprehensive Cancer Network (NCCN) guidelines, is crucial for advanced or metastatic CRC, including the evaluation of KRAS mutations, HER2 amplification, and microsatellite instability (MSI)/mismatch repair (MMR) status [27]. Therefore, investigating the gene mutation profile in CRC is essential for understanding disease prognosis and guiding personalized treatment approaches. In this investigation, we leveraged the cBioPortal database to assess the mutation frequency of FSTL1 across four colorectal cancer cohorts. Our analysis unveiled mutation frequencies ranging from .5% to 3%, predominantly characterized by missense mutations. These results imply a potential contribution of FSTL1 gene alterations to colorectal cancer development. The advent of immunotherapy has notably enhanced the prognosis of colorectal cancer patients, particularly those responsive to immune checkpoint blockade (ICB) therapy. The tumor microenvironment (TME) plays a pivotal role in modulating tumor progression, immune evasion, and treatment resistance [28–30]. Immune cells within the TME can either impede or facilitate tumor growth based on their type, activation status, and spatial distribution. The introduction of ICB therapies targeting PD-1/PD-L1 and CTLA-4 has paved the way for augmenting CD8⁺ T-cell infiltration and anti-tumor immune responses [31–33]. To investigate the immunomodulatory function of FSTL1, we evaluated immune cell infiltration in colorectal cancer specimens from the GEO dataset using single-sample gene set enrichment analysis (ssGSEA). Our findings demonstrated a significant positive correlation between FSTL1 expression and the levels of regulatory T cells (Tregs), tumor-infiltrating lymphocytes (TILs), macrophages, neutrophils, and dendritic cells. Moreover, FSTL1 expression exhibited close associations with heightened stromal scores, immune scores, and ESTIMATE-derived microenvironment scores, indicating robust interactions between FSTL1 and TME components. Intriguingly, we observed a notable negative correlation between FSTL1 expression and that of immune checkpoint genes PD-1 and CTLA-4, suggesting a potential role of FSTL1 in promoting immune evasion by recruiting inflammatory cells while suppressing effective anti-tumor immune responses.

These results underscore the plausible involvement of FSTL1 in immune surveillance regulation and tumor progression. Given the limited response of certain colorectal cancer patients to ICB therapy, we conducted a screening for potential therapeutic agents targeting the high-FSTL1-expressing population. Among the identified candidate drugs, axitinib, a multi-targeted tyrosine kinase inhibitor, emerged as a promising treatment modality that could enhance treatment outcomes for colorectal cancer patients with elevated FSTL1 expression. Tumor-associated fibroblasts (CAFs) represent the predominant stromal cell type in the TME and play a central role in tumorigenesis [34]. CAFs stimulate tumor cell proliferation and survival by secreting pro-tumorigenic growth factors such as transforming growth factor-β (TGF-β) and platelet-derived growth factor (PDGF), thereby promoting tumor invasiveness [35]. Cancer-associated fibroblasts (CAFs) modulate the immune microenvironment through the secretion of cytokines and chemokines, suppressing anti-tumor immune responses and facilitating immune evasion [36–38]. Additionally, CAFs contribute to tumor progression by remodeling the extracellular matrix (ECM) through the synthesis of structural proteins like collagen and fibronectin [39–41]. Analysis of the single-cell dataset GSE178341 from the Gene Expression Omnibus (GEO) database revealed prominent expression of FSTL1 in stromal cells. Cell-cell communication analysis identified strong interaction networks between stromal cells, myeloid cells, and B cells, primarily mediated by the APP-CD74 signaling pathway crucial for antigen presentation. These findings suggest that FSTL1 may modulate stromal cell functions to hinder antigen presentation, thereby promoting immune evasion. Given that CAFs are a key stromal component, fibroblasts were further categorized based on FSTL1 expression levels. Subsequent clustering analysis demonstrated that approximately 80% of fibroblasts were FSTL1-positive, significantly influencing the cellular composition and functional status of CAFs within the colorectal cancer (CRC) microenvironment. The prevalence of FSTL1-positive fibroblasts likely contributes to an immunosuppressive milieu conducive to tumor progression. To elucidate the role of FSTL1 in tumor development, RNA splicing rate analysis was employed to differentiate between unspliced and maturely spliced mRNAs, providing insights into the directionality of cell differentiation [42]. Results obtained using the VECTOR algorithm indicated a substantial portion of stromal cells in an early differentiation stage, suggesting that FSTL1 may drive initial tumor-promoting activities in the stromal cell population. Despite these findings, our study is constrained by its reliance on GEO dataset analysis and necessitates validation in a larger independent cohort. Furthermore, while in vitro experiments have confirmed FSTL1's impact on CRC cell behavior, in vivo studies are imperative to unravel the precise mechanisms underlying FSTL1-mediated immune cell infiltration and CRC progression. Addressing these aspects will guide our future research endeavors.

Conclusion

In summary, our study indicates that overexpression of FSTL1 predicts poor

prognosis and clinical stage progression in patients with colorectal cancer. It has been linked to FSTL1 and the expression of immune checkpoints and immune cells in colorectal tumors. So, FSTL1 may be a key player in controlling tumor immunity and growth, and it has great potential as a biomarker and therapeutic target for colorectal cancer (CRC).

Abbreviations

CRC

Colorectal Cancer

TCGA

The Cancer Genome Atlas

GEO

Gene Expression Omnibus

GSEA

Gene Function Enrichment Analysis

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

COAD

Colon Adenocarcinoma

READ

Rectum Adenocarcinoma

OS

Overall Survival

TME

Tumor microenvironment

Author contributions

ZC and CZR performed data analyses. LY and CXX and SYS performed experiments. LXS and YXJ acquired tissues and data. HL and HJ developed the study design, interpreted data, and supervised the study. All the authors contributed to writing the manuscript.

Funding

^{This study is funded by the Scientific and Technological Research Project, Fund of the Education Department of Jiangxi Province (Project Number GJJ2200989)}

Data availability

The data that support the findings of this study are openly available in Gene Expression Omnibus (GEO) at https://www.ncbi.nlm.nih.gov/geo/, reference number GSE16125，GSE29621，GSE178341.

Declarations

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.